NDLI: Location estimation in large indoor multi-floor buildings using hybrid networks

Content Provider	IEEE Xplore Digital Library
Author	Kejiong Li Bigham, J. Bodanese, E.L. Tokarchuk, L.
Copyright Year	2013
Description	Author affiliation: Sch. of Electr. Eng. & Comput. Sci., Queen Mary, Univ. of London, London, UK (Kejiong Li; Bigham, J.; Bodanese, E.L.; Tokarchuk, L.)
Abstract	This paper presents results for an approach for indoor location estimation that integrates received signal strength (RSS) data from both WiFi and GSM networks. Previous work has focused on relatively small indoor environments. In many potential applications, getting approximate location information, such as in which room the mobile user is, is adequate. A hierarchical clustering method is used to partition the RSS space. To choose the best transmitters in a partition, we assess the amount of RSS variance that is attributable to different base stations (BSs) or access points (APs) by transforming the RSS tuples into principal components (PCs). This allows us to retain most of the useful information of detectable transmitters in fewer dimensions. In our experiments, we collected WiFi and cellular RSS on the 2nd and 3rd-floor electronic engineering (EE) building in Queen Mary campus. The experiment results show that the proposed method can provide a good accuracy of room prediction, especially when we integrate WiFi RSS with GSM RSS together to do the positioning.
Starting Page	2137
Ending Page	2142
File Size	535498
Page Count	6
File Format	PDF
ISBN	9781467359382
ISSN	15253511
e-ISBN	9781467359399
e-ISBN	9781467359375
DOI	10.1109/WCNC.2013.6554893
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-07
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	IEEE 802.11 Standards GSM Accuracy Estimation Training Transmitters Training data
Content Type	Text
Resource Type	Article

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